An intelligent model based on statistical learning theory for engineering rock mass classification

Abstract

The engineering classification of rock masses is the basis of rock engineering design and construction. We propose and apply a quick basic quality (BQ) classification method based on the standard BQ method of China to classify the quality grade of the rock mass around tunnels along the Ningguo-Huangshan Expressway during the construction period. Moreover, the joint continuity and surface roughness of the controlled key joint are added to the classification indices of the quick BQ method to address shortcomings of the standard BQ classification method. Therefore, an improved BQ classification method for rock mass is proposed. According to the BQ method, different personnel might select different values of correction coefficient that result in divergences in the result of rock mass classification. In order to solve this problem, the Genetic algorithm (GA) and support vector classification (SVC) coupling algorithm is introduced into the field of engineering rock mass classification. GA is used to automatically search for the optimal SVC parameters during the training process of samples. By training the classification samples of rock mass around a tunnel using the improved BQ method during the tunnel construction period, an intelligent SVC classification model is constructed with inputs based on eight classification indices and an output of the BQ quality grade. To verify the reliability and accuracy of the model, the SVC model is used to evaluate the quality grade of the rock mass around tunnel in other cross sections of the tunnels along the Ningguo-Huangshan Expressway. Only one section classification result differed from those of the improved BQ method in a total of 20 sections. In contrast, three section classification results based on the BP neural network (BPNN) model were inconsistent with those of the improved BQ method. Therefore, the proposed SVC model displays a higher rate of correct classification relative to that of the BPNN model. Meanwhile, the use of this SVC model can avoid the divergence among different people on the classification result of rock mass around a tunnel, which provides an effective new method for the rapid classification of rock mass around a tunnel during tunnel construction.